The invention relates generally to printing mechanisms and more particularly to a system for determining the type of print media, so that the printing mechanism can automatically select an optimal print mode for a specific type of incoming media without requiring user intervention.
For printers on the commercial market today, such as laser and inkjet printers, automated selection for the type of print media (e.g., transparency media, premium media, glossy photo media, matte photo media, etc.) is not always present. Rather than using a close-loop feedback system for automated selection, these printers use an open-loop process by relying on a user to select the type of print media through the software driver in his/her personal computer (PC). Without correctly selecting the proper type of print media, there is no assurance that the media corresponds to the type selected for a particular print request. Consequently, the type of print media used for printing may not always correspond to an optimal operational mode of the printer.
Printing with an incorrectly selected media often produces poor quality images. The problem primarily stems from the fact that most users do not change the media type settings, even assuming that they are aware of the existing settings. Instead, the typical users print with a default setting of the plain paper-normal mode. This is unfortunate, because if a user inserts an expensive photo media into the printer, the resulting image is sub-standard when the normal mode rather than a photo mode is selected, leaving the user effectively wasting the expensive photo media. Besides photo media, other types of media such as transparencies yield particularly poor image quality when they are printed in the plain paper-normal mode.
One proposed system for a printer to automatically adopt an optimal print mode for a specific type of incoming media without requiring user intervention utilizes an invisible ink code. The code is printed on each sheet of incoming media where it is read by a sensor onboard the printer. The code supplies the printer driver with practical information, such as the media type, manufacturer, orientation and properties. Armed with this information, the system is both reliable and economical in properly selecting the correct type of print media for optimal performance. Thus, the user is no longer burdened by media selection through his/her PC. A concern with the invisible ink code system is that the pre-printed invisible code can become visible when printed over. To avoid this problem, the code is placed at the margin of the print medium. However, since market demand is pushing printers into becoming high-quality photo generators, the invisible code becomes an undesirable artifact for a photographic finish requiring printing up to the edge of the paper. Consequently, placing the invisible code at the margin creates a print defect for printing in the photo-mode.
Another system for print media type determination utilizes a combination of transmissive and reflective sensors. The transmissive sensor measures the amount of light that has passed through the print media and is very effective for some media type determinations, such as the identification of a transparency. The reflective sensors receive light reflected off the surface of the print medium at different angles and are used to measure the specular reflectance and the diffuse reflectance of the medium. By analyzing the ratio of these two reflectance values, a specific medium type is identified. To implement this system, a database having a look-up table of the reflective ratios is used to correlate the ratios with various types of print media. A concern with this system is that new, non-characterized medium is often misidentified, leading to print quality degradation. Another concern is that several different types of media could generate the same reflectance ratio, yet have different print mode classifications.
What is needed is a method and system for reliably determining the type of incoming print medium, so that the printing mechanism can automatically select a proper print mode without requiring user intervention.
The invention is a method and system that uses neural network techniques for automatically selecting a print medium type without requiring user intervention. A media detection system captures data indicative of characteristics of an incoming medium. The data is spectrally analyzed to derive frequency-related information. At least one media-identifying neural network utilizes the frequency-related information to determine a print medium type. A xe2x80x9cneural networkxe2x80x9d is herein defined as an adaptive arrangement which is specifically designed to adapt on the basis of prior decisions in order to increase the accuracy of decisions. Utilizing a feedforward architecture, the media-identifying neural network includes a layer of decision making nodes (i.e., the xe2x80x9chiddenxe2x80x9d layer). Each decision making node includes an activation function for processing a sum of multiple weighted inputs to the node. The output from each decision-making node may be directed to a node within the same layer for continuous processing or to a node in an output layer. Each node at the output layer corresponds to a major type of print medium selection, including a transparency type, premium-paper type, plain-paper type, photo-quality type, and default type. Subsequent to identifying the print medium as one of the major medium types, a specific neural network is utilized to narrow the identified type of medium into a more specific category.
The media-identifying neural network comprises an input layer of nodes, an output layer of nodes and one xe2x80x9chiddenxe2x80x9d layer of nodes sandwiched between the input and output layers. In a first embodiment in which a major network is used to identify an incoming print medium as one of the five major media print types, each node of the input layer is configured to receive one frequency component from the media detection system. Each frequency component is derived by spectrally analyzing (e.g., performing Fourier Transform) the data captured by the media detection system. If there are 84 diffuse frequency components and 84 specular frequency components, the input layer comprises 168 input nodes, with each node being configured to receive one frequency component and to impose a weight on the received component.
The outputs from the input nodes are directed to the xe2x80x9chiddenxe2x80x9d or decision-making layer. Actual computations utilizing algorithms are performed at the decision-making layer to determine a print medium type. The optimal number of decision-making nodes utilized in this layer is dependent on the nature of the classification. A task requiring greater accuracy may use a greater number of decision-making nodes, while a task requiring greater speed may use a fewer number of nodes. In one embodiment, the decision-making layer comprises at least six decision-making nodes. In a second embodiment, the layer comprises at most ten decision-making nodes. Each decision-making node may be configured to receive 168 weighted inputs and emit one output. An activation function is applied to the sum of the weighted inputs, together with a bias weight for each decision-making node to produce one output.
The decision-making nodes are configured to generate a decision for designating a print medium type for the incoming medium. Each of the nodes in the output layer corresponds to one of the major media types. While the process may designate the subject print medium as one of a transparency type, premium-paper type, plain-paper type, photo-quality type and default type, other types of categorization can be selected without diverging from the scope of the invention.
In the first embodiment, the print medium is further subjected to analysis within a specific neural network to differentiate the selected major media type into narrower categorizes. For example, after a determination by a major network that an incoming print medium is a xe2x80x9cphoto-quality type,xe2x80x9d a specific neural network is utilized to further differentiate the xe2x80x9cphoto-quality typexe2x80x9d as one of a: (1) default type, (2) Gossimer type, (3) combined type, and (4) very glossy type.
In a second embodiment, the 168 frequency components are analyzed to determine a print media type of the incoming print medium utilizing other categorizing means, without being subjected to analysis within a major neural network. Specifically, after identifying the print medium as one of the major media types utilizing other categorizing techniques, the incoming medium is subjected to the specific neural network to more clearly differentiate the medium as being one that fits within a narrower category.
The media-identifying network architecture is dependent on the types of training algorithms used for defining the network. During training in the xe2x80x9csupervisedxe2x80x9d mode, a training set of print media for a particular class (e.g., a transparency type) is provided to the printing mechanism. The decision-making nodes are set to be xe2x80x9cONxe2x80x9d for that particular class and xe2x80x9cOFFxe2x80x9d for the other classes. Each node is associated with a bias term, i.e., a weight, to be applied to each input value. A weight determines how much relative effect an input value has on an output value for a given node. Initially, the values for the weights are selected at random. As training continues, error reduction algorithms adjust the actual outputs to the target outputs by reducing the error space for each of the connections in the network. The adjustment utilizes a genetic algorithm or a simulated annealing algorithm to determine a global minima for each connection. An associated weight corresponding to the global minima reduces the measure of error in the network""s results. Finally, a conjugate descent is performed to determine the direction of the global minima. The training process continues until the error value is within an acceptable target range.
In one aspect of the invention, an incoming print medium that does not correspond to one of a desired type (i.e., transparency type, premium-paper type, plain-paper type and photo-quality type) is directed to an output node designated as the default type. A faulty training set of print media that does not correspond to one of the desired types may be input to the printing mechanism to teach the system to recognize a non-desired type of incoming print medium.
One of the advantages of the invention is that by utilizing a media-identifying neural network, the system is flexible and can easily be updated to detect other types of print media.